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Record W7061507451

Reading the signs in the Whitefeather Forest cultural landscape, northwestern Ontario

2008· dissertation· en· W7061507451 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2008
Typedissertation
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCorporationCitizen journalismNatural resource managementNatural resourceReading (process)NarrativeNatural (archaeology)Corporate governanceProcess (computing)Traditional knowledge
DOInot available

Abstract

fetched live from OpenAlex

s boreal forest, natural resources planning, management and governance are increasingly becoming shared amongst different cultural groups.Criteria and lndicators (C&l) have become a leading tool for assessing sustainability, for guiding natural resources management planning and decision-making, and for monitoring ecological change.However, there are few examples of 1) the processes by which a shared understanding of lndigenous C&|, grounded in local knowledge, values and institutions can be developed, and2) what a local-levelAboriginal C&l framework, grounded in local values and institutions, would look like.This research, undertaken collaboratively with Pikangikum First Nation (PFN) and the Whitefeather Forest Management Corporation (WFMC), attempts to address these knowledge gaps through the following objectives:1. Develop an understanding of Pikangikum values for Keeping the Land, and the institutions through which they are fostered and actualized (i.e.Ahneesheenahbay ways of knowing, practices and beliefs).2. Cooperatively develop a framework to both articulate and communicate the values, knowledge and institutions for Keeping the Land.3. Develop an understanding of how these values represent criteria for Keeping the Land, how Pikangikum people perceive these signs (i.e.indicators) of social-ecological variability in the Whitefeather Forest, and how these sgns contribute to: a. Monitoring, responding and adapting to change, and b.Maintaining the values, knowledge and institutions for Keeping the Land.Methods for this undertaking included review of narratives gathered throughout the Whitefeather Forest community-based land use planning process as well as collaborative workshops with community lders.Approached from a cooperative learning perspective, the research was participatory and iterative in nature.This approach allowed for the co-production of a holistic cultural landscape framework and the development of shared understandings of the values and institutions for Keeping the Land.Keeping the Land must begin with Ohneesheesheen, to have good mental, spiritual, physical, emotional health, and practice activities properly on the land to create well- being in yourself and in your actions.To be able to create Cheemeenooweecheeteeyaung, to build good relationships with family, community, and the Creator and to form partnerships with people from other cultures, everything must be good.These relationships, in turn, are what make Oohnuhcheekayween possible (i.e.planning for the future, and making decisions for the community that will have positive social, economic, and environmental outcomes).This planning and decision-making will ensure that Ailneesheenahbayweepeemahteeseeween, the Pikangikum way of life, will continue as it should and that the land will continue to be kept.As criteria are values, and indicators arise from values (Meadows 1998), the cultural landscape framework was also developed into a local-level approach to monitoring Keeping the Land.Pikangikum's approach to criteria and indicators (C&ls) are based on held values embedded in Ahneesheenahbay worldview, beliefs, and rules of proper conduct with the land.This study presents an example of a place-based learning community, where collaborative learning resulted in the co-production of new knowledge.This knowledge is based on a shared understanding of Pikangikum values and institutions for Keeping the Land, and as such can contribute to building a new approach to Natural Resources and Environmental Management (NREM).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.184
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2008
Admission routes2
Has abstractyes

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